The human eye features three types of cones that are sensitive to different parts of the visible spectrum. These cones are usually designated as L, M and S, referring to the wavelengths they sense (long, middle and short), which roughly correspond to red, green and blue colours. Relative spectral sensitivities of the cones are illustrated in FIG. 1 which illustrates that the cones have a fairly broadband character and correspond to an integration of the light over a wide wavelength range. Consequently, it is possible for two materials with different spectral signatures to appear to the human observer as having the same colour in certain light conditions. This phenomenon is known as metamerism. Similar to the human eye, three primary (RGB) systems employing broad colour filters have become main-stream for both displays and cameras. Displays rely on appropriate mixing of the primary colours to generate any colour within the gamut enclosed by the primaries.
It is often beneficial to characterize images on the basis of a more detailed spectral reflectivity than that provided by (relatively coarse) RGB colour coordinates. It is furthermore desired that an image is captured with local spectral information, i.e. where the spectral characteristics of different parts of the image are individually characterized. Such, imaging is known as multi-spectral imaging and is a technique which has found many practical applications, including for example:                Contaminant Detection        Environmental Monitoring        Grain/Timber Grading        Microorganism Detection (Fluorescence/Cytometry)        Flow Cytometry        Oximetry, etc.        
For some applications it is desirable to analyse only specific portions of the visible spectrum. For example, in photoplethysmography, the heart rate of a human is derived from time-analysis of an optical recording. It is, however, well-established that the heart-rate signal is strongest for green colours (e.g., 540-560 nm) due to the spectral absorption properties of haemoglobin. As a result, a system specifically analysing the narrow spectral band of interest will provide more accurate estimates than a system employing broad-band sensors that pick up more non-specific signals of the surroundings and noise.
It is desirable for a multi-spectral camera to provide both high spatial resolution, high spectral resolution and high temporal resolution. However, these requirements tend to be contradictory and therefore a trade-off between the different requirements is often necessary.
One type of multi-spectral cameras uses an approach wherein the scene/target is scanned line by line, and orthogonal to this line, a dispersive element (such as a grating or a prism) is used to extract the spectrum of every pixel within the line. The resulting two dimensional data (with one spatial and one spectral dimension) is captured using a conventional two dimensional sensor. The full three dimensional data (two spatial dimensions and one spectral dimension) is then built up by gradually and sequentially scanning the lines in the direction perpendicular to the line.
However, such a camera tends to be relatively complex and require a mechanical movement to implement the scanning. This tends to result in increased complexity, increased cost, reduced reliability, increased power consumption and increased size and/or weight. The required scanning process also tends to be relatively slow resulting in a relatively long time to capture an image. This makes the approach less suitable e.g. for capturing moving images.
Another type of multi-spectral cameras uses a variable spectral filter which is placed in front of a normal black and white camera. By sequentially changing the filters and recording the corresponding image, the full three dimensional data can be acquired (i.e. each captured image will correspond to the light in the passband frequency interval of the filter). A major drawback of this approach is that the light-efficiency appears to be rather poor since a lot of light is blocked by the filter. Moreover, suitable filters, such as liquid crystal tunable filters and acousto-optical tunable filters, are rather expensive and usually only allow a single wavelength of light to pass through (notch pass). The approach also tends to have the same disadvantages as the scanning multi-spectral cameras, i.e. to be slow, have relatively low reliability etc.
A particularly important disadvantage with these types of multi-spectral cameras is that they trade spectral resolution for temporal resolution. This is a disadvantage in situations where the imaged objects are moving. Furthermore, the methods generally have very specific (fixed) spectral resolutions that cannot easily be adapted to the application.
Hence, an improved multi-spectral camera would be advantageous. For example, a multi-spectral camera allowing increased flexibility, reduced cost, reduced complexity, increased reliability, reduced size/weight, reduced power consumption, improved temporal performance/resolution and/or improved performance would be advantageous.